Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow, needing several milliseconds per image using modern GPUs. The cost of content-based queries over a huge video corpus is prohibitive. A promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the input image format and the requisite data handling costs as part of our query optimization, we can enable much more efficient cascades. We find that by jointly optimizing the CNN architecture and input representation, we can provide up to a 40x speedup over the cascades used in the NoScope video query system. We find up to a 156x speedup over ResNet with no accuracy loss and a nearly 300x speedup for users willing to sacrificing some accuracy.